Senior Data Engineer - Build Scalable Data Platforms

Contact Energy; Wellington; On-site; NZD 130,000 - 170,000; Full time

Benefits offered by this job

Wellbeing benefits

Flexible working

KiwiSaver 4%

Corporate discounts

Job summary

Contact Energy’s Data Engineering team seeks a Senior Data Engineer to help build and improve our data platform, enabling trusted, scalable insights across the business. You’ll work with experienced data engineers on behind-the-scenes improvements that support our business-facing data squads and deliver practical results.

You’ll contribute to platform enhancements, data pipelines, and ML-ready workflows while collaborating across squads, embracing ambiguity and helping the team move forward with

Qualifications

• Strong SQL capability
• Experience with Linux and Git
• Proficiency in Python and Spark
• Experience building, supporting and improving data pipelines and data platforms
• Ability to troubleshoot complex technical challenges and work through ambiguity
• Collaborative mindset and willingness to mentor others
• AWS experience
• Machine learning pipeline experience
• SAP exposure
• Experience coaching or mentoring other engineers

Responsibilities

• Collaborate with data engineers on behind-the-scenes platform improvements
• Work on platform enhancements and data pipelines to support business squads
• Help unblock work and deliver practical, visible impact
• Contribute as part of a high-trust, collaborative team across Aotearoa

Skills

SQL

Linux

Git

Python

Spark

Data pipelines

Mentoring

Team collaboration

AWS

Databricks

SAP

ML pipelines

Tools

AWS

Databricks

SAP

Job description

Contact Energy’s Data Engineering team seeks a Senior Data Engineer to help build and improve our data platform, enabling trusted, scalable insights across the business. You’ll work with experienced data engineers on behind-the-scenes improvements that support our business-facing data squads and deliver practical results.

You’ll contribute to platform enhancements, data pipelines, and ML-ready workflows while collaborating across squads, embracing ambiguity and helping the team move forward with